Antimicrobial resistance among common bacterial pathogens in Indonesia: a systematic review

Summary Background The WHO Global Antimicrobial Resistance Surveillance System (GLASS) aims to describe antimicrobial resistance (AMR) patterns and trends in common bacterial pathogens, but data remain limited in many low and middle-income countries including Indonesia. Methods We systematically searched Embase, PubMed and Global Health Database and three Indonesian databases for original peer-reviewed articles in English and Indonesian, published between January 1, 2000 and May 25, 2023, that reported antimicrobial susceptibility for the 12 GLASS target pathogens from human samples. Pooled AMR prevalence estimates were calculated for relevant pathogen-antimicrobial combinations accounting for the sampling weights of the studies (PROSPERO: CRD42019155379). Findings Of 2182 search hits, we included 102 papers, comprising 19,517 bacterial isolates from hospitals (13,647) and communities (5870). In hospital settings, 21.6% of Klebsiella pneumoniae isolates, 18.3% of Escherichia coli isolates, 35.8% of Pseudomonas aeruginosa isolates and 70.7% of Acinetobacter baumannii isolates were carbapenem-resistant; 29.9% of Streptococcus pneumoniae isolates were penicillin-resistant; and 22.2% of Staphylococcus aureus isolates were methicillin-resistant. Hospital prevalence of carbapenem-resistant K. pneumoniae and E. coli, and penicillin-resistant S. pneumoniae increased over time. In communities, 28.3% of K. pneumoniae isolates and 15.7% of E. coli isolates were carbapenem-resistant, 23.9% of S. pneumoniae isolates were penicillin-resistant, and 11.1% of S. aureus isolates were methicillin-resistant. Data were limited for the other pathogens. Interpretation AMR prevalence estimates were high for critical gram-negative bacteria. However, data were insufficient to draw robust conclusions about the full contemporary AMR situation in Indonesia. Implementation of national AMR surveillance is a priority to address these gaps and inform context-specific interventions. Funding 10.13039/100004440Wellcome Africa Asia Programme Vietnam.


Introduction
Antimicrobial resistance (AMR) is a global public health priority with significant economic consequences and a disproportionate impact in low and middle-income countries (LMICs). 1 Antibiotic-resistant bacterial infections have been estimated to be associated with 4.95 million deaths worldwide in 2019 alone. 2Southeast Asia has been identified as a region of great importance in the development and spread of AMR, driven by overuse and misuse of antimicrobial agents, poor sanitation, hygiene and infection control, and other health system vulnerabilities to AMR. 3,4 In studies from Thailand, 5 Cambodia, 6 and Indonesia, 7 community and hospitalacquired drug-resistant bacteraemias have been associated with increased mortality and hospital costs.
National and global AMR surveillance systems are important to provide evidence for empiric treatment guidelines, estimate epidemiological burdens across time and space, and assess the impact of public health policies and interventions.To mitigate the impact of AMR, in 2015 the World Health Organization (WHO) coordinated the development of the Global Antimicrobial Resistance Surveillance System (GLASS), as a component of the Global Action Plan on AMR. 8 However, despite progress, many LMICs have not been able to aggregate data to provide a representative countrylevel perspective.
Indonesia is a vast archipelagic nation with the world's fourth largest population (275 million), stark socio-economic and health inequalities, and a decentralised healthcare system with weakly enforced antibiotic regulations. 9Limited available data have suggested high levels of AMR against key antibiotics for common clinically important, especially gram-negative, bacterial pathogens in hospital populations, 8,10 and an estimated 34,530-133,753 deaths because of bacterial AMR in 2019. 11This systematic review aimed to aggregate all available data in the human health sector published in the peer-reviewed literature in Indonesia since the year 2000, to estimate AMR prevalence for relevant pathogen-antimicrobial combinations, focusing on the twelve GLASS target bacterial pathogens, i.e.Acinetobacter baumannii, Escherichia coli, Haemophilus influenzae, Klebsiella pneumoniae, Neisseria gonorrhoeae, Neisseria meningitidis, Pseudomonas aeruginosa, Salmonella enterica serovar Typhi and Paratyphi A (typhoidal), Salmonella spp.(non-typhoidal), Shigella spp., Staphylococcus aureus, and Streptococcus pneumoniae, both for hospital and community settings. 12

Search strategy and selection criteria
This systematic review was reported according to the Preferred Reporting Items for Systematic Reviews and

Research in context
Evidence before this study We reviewed situation analysis reports, the WHO Global Antimicrobial Resistance Surveillance System (GLASS) website, and searched PubMed with the terms "Indonesia", "antibiotic or antimicrobial resistance", and "human health", until November 1st 2023, in English and Indonesian language.Indonesia is a potential hotspot for antimicrobial resistance (AMR) because of high infectious disease burdens, coupled with weakly enforced antibiotic regulations in human and veterinary health, and other health system vulnerabilities.Two recent reports aggregated routine culture data from sentinel hospitals (GLASS submission, 20 hospitals, and Indonesian Society for Clinical Microbiology, 70 hospitals, both from 2022), describing high prevalence estimates for third-generation cephalosporin (3GC) (53-79%) and carbapenem (29-30%) resistant Klebsiella pneumoniae; 3GC (66-70%) and carbapenem (15-15%) resistant Escherichia coli; carbapenem-resistant Acinetobacter spp.(76-89%); methicillin-resistant Staphylococcus aureus (38-40%); ceftazidime (41%) and carbapenem (45%) resistant Pseudomonas aeruginosa; and penicillin-resistant Streptococcus pneumoniae (20%) (Panel).A comprehensive review on AMR in human health in Indonesia has not been conducted to date.

Added value of this study
This review represents an aggregation of the peer-reviewed literature on the magnitude and patterns of AMR in common bacterial pathogens most relevant to human health in Indonesia, including nearly 20,000 isolates from 102 studies spanning the past 23 years.This countrywide evidence synthesis can be a guiding tool for policymakers and medical practitioners.Hospital-based levels of AMR to important antibiotics in common gram-negative bacteria were estimated to be among the highest reported in the Southeast Asia region.These included carbapenem-resistant Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli and Klebsiella pneumoniae, all classified as Priority 1 ("critical") bacteria on the WHO bacterial priority pathogen list for research and development of new antibiotics.The review identified areas where critical information is lacking, particularly on specific bacterial pathogens, community settings, geographic settings outside of Java island, as well as clinical metadata for classification of isolates.

Implications of all the available evidence
The management of severe infections associated with gramnegative bacteria in Indonesia is increasingly dependent on more expensive and less readily available antibiotics.Accelerated implementation of national AMR surveillance and the National Action Plan for AMR containment is an urgent priority.
Meta-analyses (PRISMA) 2020 guidelines.The protocol was registered in PROSPERO (CRD42019155379).We searched three international (PubMed, Embase and Global Health Database) and three Indonesian (Garba Rujukan Digital, Neliti and Jurnal Penelitian dan Pengembangan Pelayanan Kesehatan) bibliographic databases for eligible articles, published since January 1, 2000 until May 25, 2023.The searches combined the terms "Indonesia", "antimicrobial or antibiotic resistance", "susceptibility", "resistance", plus each of the bacterial pathogens included in the GLASS-AMR Manual 2.0 (Supplementary Table S1), 12 restricted to original, peer-reviewed articles in English or Indonesian language.Reports were included if they reported bacterial antibiotic susceptibility testing (AST) on isolates from human specimens collected in Indonesia, regardless of clinical context, and reporting on at least one GLASS bacterial pathogen.Reports were excluded if they were written as reviews, editorials, conference proceedings or individual case reports; completed data collection before 2000; did not report phenotypic AST data for at least one GLASS pathogen-antimicrobial combination or addressed only non-GLASS organisms; AST data had missing numerators/denominators that could not be calculated from raw data; there were fewer than 10 isolates tested for AST; addressed only samples from non-human sources; or Indonesian and non-Indonesian data could not be separated.

Article screening and selection
Two independent reviewers (MG, GL, DS, RS) screened titles and abstracts of all articles and manually removed duplicate articles and/or datasets.Full-text articles were independently judged for relevance and quality by at least two reviewers (MG, GL, DS, RS).Any disagreements were resolved by a senior researcher (RLH).

Risk of bias and quality assessment
Because the included studies were neither randomised controlled trials nor comparative studies, traditional methods for assessment of risk of bias were not applicable.Instead, we excluded studies that did not report on an essential set of four core items from the STrengthening the Reporting of Observational studies in Epidemiology (STROBE) checklist (Supplementary Table S2).We additionally recorded the reporting for the 13 mandatory items of the Microbiology Investigation Criteria for Reporting Objectively (MICRO) checklist (Supplementary Table S3). 13

Data extraction and synthesis
Data were extracted by two reviewers (MG, GL, DS, RS) onto a predefined extraction form.We extracted and tabulated data on general article information (first author, year of publication), and author-reported summary estimates (not individual-level data) of the AST results for relevant pathogen-antimicrobial combinations (GLASS and additional clinically relevant combinations) (Supplementary Table S4), specimen type (e.g.blood, urine, stool), study population (health status, age group, sex), health care setting (hospital or community), geographic location, period of data collection, and laboratory method information.Studies in communities, primary care, and outpatient clinics were classified as community setting.Studies among inpatients, as well as studies among mixed or unspecified inpatients and outpatients, were classified as hospital setting.For hospital-based studies, as per GLASS, hospital-acquired infection (HAI) was defined as samples taken >2 calendar days after date of admission, and communityacquired infection (CAI) was defined as samples taken ≤2 calendar days after date of admission.

Ascertainment of AMR
Given that most studies did not report minimal inhibitory concentrations (MICs) for antimicrobials, we used author-reported AST interpretations into susceptible, intermediate or resistant.Intermediate susceptibility, where reported, was considered resistant.Resistance proportions were calculated as the inverse of susceptibility where applicable.AST interpretative criteria used were extracted (e.g.Clinical and Laboratory Standards Institute [CLSI], European Committee on Antimicrobial Susceptibility Testing [EUCAST]).Duplicate or sequential isolates, when reported, were removed from the analysis.Where AST results were given for more than one sameclass antibiotic (e.g.third-generation cephalosporins [3GC], fluoroquinolones, group 2 carbapenems [doripenem, imipenem, meropenem]), the highest individual-antibiotic resistance proportion was selected to represent class resistance, except for the aminoglycosides since gentamicin, amikacin and tobramycin show markedly different resistance patterns. 12Resistance of S. aureus to cefoxitin, methicillin and/or oxacillin were considered methicillin-resistant (MRSA).

Statistical analysis
Because of substantial heterogeneity in study design, populations studied, period of data collection, and type of specimens collected, we considered quantitative meta-analysis inappropriate. 14Instead, pooled AMR prevalence estimates were calculated for relevant pathogen-antimicrobial combinations, as the number of patients with infection caused by pathogen x resistant to antibiotic y , divided by total number of patients with infection caused by pathogen x with AST results for antibiotic y (either susceptible, intermediate or resistant), and expressed as percentages, 12 accounting for the sampling weights (number of isolates) of the individual studies, separately for hospital and community settings.We calculated 95% confidence intervals using the Wilson score interval when the number of studies for a pathogen-antimicrobial combination was more than one.Additionally, we calculated AMR prevalence for pathogen-antimicrobial combinations per five-year time periods from 2000 to 2023, where sufficient data were available.Data management was performed using Microsoft Excel ® (Microsoft Corp., Redmond, WA) and visualisations using GraphPad Prism version 9.5.0 (GraphPad Software, Boston, MA).

Role of the funding resource
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.The first and corresponding authors had full access to all the data and the final responsibility to submit for publication.

Identification of studies via databases and registers
Proportions of resistance for each of the pathogen-antimicrobial combinations Klebsiella pneumoniae 42 reports described 2620 K. pneumoniae isolates, including 39 hospital reports (2373 isolates) and 7 community reports (247 isolates).

Time period AMR prevalence (%)
Fig. 5: AMR prevalence for pathogen-antimicrobial combinations in selected gram-negative GLASS pathogens in hospitals per 5-year time periods.Figure shows AMR prevalence estimates per 5-year time periods for GLASS-specific pathogen-antimicrobial combinations per time period in selected gram-negative GLASS pathogens in hospitals, accounting for sampling weights of the individual studies (see Supplementary Table S4 for further details).The dots were connected by lines to improve interpretation of the data.Pathogens for which insufficient data were available are not shown.For studies that collected data for two years, the latest year was used as the year of data collection.For studies that collected data for more than two years, the median of the period of data collection (rounded to the nearest integer) was used as the year of data collection.95% confidence intervals were estimated using Wilson score interval (where the number of studies was greater than one).a The estimate for third-generation cephalosporins is derived only from ceftazidime.erythromycin 28.6% (24.5-33.0;122/427) (Fig. 4, Supplementary Fig. S1 and Table S11).Across the five hospital reports that distinguished HAI and CAI, the percentage of S. aureus isolates exhibiting resistance against methicillin was unknown (no HAI data) and 62.5% (5/8); vancomycin 7.2% (4.9-10.5;24/324) and 0.0% (0/9); clindamycin 17.0% (13.3-21.3In communities, the percentage of S. aureus isolates exhibiting resistance against methicillin was 11.1% (95% CI 8.3-14.7;9/367); vancomycin 2.2% (0.9-5.5; 4/185); clindamycin 9.6% (5.9-15.4%;10/150); and erythromycin 10.4% (7.5-14.3;14/317).

Neisseria meningitidis
None of the included reports reported resistance data for N. meningitidis.

Discussion
The best represented GLASS target bacterial pathogens in this systematic review were E. coli, K. pneumoniae, P. aeruginosa, S. aureus, A. baumannii, and S. pneumoniae, whereas data were limited for Shigella spp., typhoidal and non-typhoidal Salmonella, N. gonorrhoeae, and H. influenzae, and there were no reports included for N. meningitidis.Although the differential representation of the GLASS target pathogens may partly reflect differences in their disease burdens, the data likely overrepresented hospital settings (72% of studies), given that microbiology laboratories and clinical services in Indonesia often prioritise taking bacterial cultures in hospitalised patients, over testing infections in community settings (24% of studies), such as for diarrhoeal illness and genito-urinary infections. 15,16The evidence base was found to be uneven with around 76% of studies from Java, leaving other geographic areas underrepresented.Gaps in classification metadata (e.g.CAI/HAI, hospitalisation status) limited the ability to draw firm conclusions.Nearly half of the publications appeared during the most recent five years, reflecting the growing importance of AMR on the national and global health agenda.There is an urgent need to include the GLASS target pathogens for which existing data were limited for Indonesia in future AMR surveillance and research.This is particularly true for Shigella spp., accounting for 13% of diarrhoeal deaths globally, 17 and N. gonorrhoeae, causing substantial morbidity in LMICs, 18 and which are both known for rapid development of AMR.
The overall AMR prevalence estimates were higher in hospitals, compared to community settings, as expected, most likely due to more and broader-spectrum antibiotic use (hence selection pressure) among vulnerable patients, coupled with poor infection and prevention control.AMR estimates for common bacterial pathogens to many of the most accessible and widely used antibiotics in Indonesian hospitalised populations were among the highest reported for the Southeast Asia region, 8,[19][20][21] and higher than reported for the European Union. 22These findings largely corroborate previous reports on culture data from routine clinical practice in sentinel Indonesian hospitals in 2022, which were not included in this review (Panel). 8,10This review found that in hospital settings, around 22% of K. pneumoniae, 18% of E. coli, 36% of P. aeruginosa and 71% of A. baumannii isolates were carbapenem-resistant, all classified as Priority 1 ("critical") on the WHO priority pathogen list for research and development of new antibiotics. 23Moreover, the rising resistance levels to carbapenems in K. pneumoniae and E. coli over time means that the management of severe infections associated with Enterobacterales is increasingly dependent on more expensive and less readily available antibiotics.Concerningly high levels of carbapenem-resistant A. baumannii have been reported across Southeast Asia, 8,[19][20][21] posing an emerging threat to hospitalised populations globally. 24,25Across the subset of studies that reported on resistance mechanisms, the proportion of ESBL-producing isolates was 60.1% for E. coli (95% CI 56.3-63.7;6 reports), and 67.4% for K. pneumoniae (95% CI 63.7-71.0;8 reports), which was in the same range as the overall prevalence among included hospital studies of 3GC-resistance (considered a proxy for ESBL), at 66.4% (95% CI 64.0-68.6;31 reports) for E. coli and 74.4% (95% CI 72.3-76.4;27 reports) for K. pneumoniae.In a study of E. coli and K. pneumoniae isolates in Surabaya, bla CTX-M-15 was the predominant ESBL-gene. 26ith regards to carbapenemases, studies in intensive care patients in Jakarta have identified the bla NDM gene as most frequent in K. pneumoniae isolates, 27 bla VIM , bla IMP and bla GES-5 gene in P. aeruginosa isolates, 28 and bla OXA-23 -like gene in A. baumannii. 29n Indonesian hospital populations, penicillinresistant S. pneumoniae (30%) was higher than reported in the Philippines 21 and Malaysia, 19 but lower than in Thailand 8 and Vietnam 20 ; and methicillin-resistant S. aureus (22%) was lower than in previous reports (38-40%), 8,10 as well as in most other countries in the region (Panel). 8,20,21With regard to community-based studies, available data suggested considerable AMR levels (e.g.carbapenem-resistant E. coli and K. pneumoniae at 16% and 28%, respectively; penicillin-resistant S. pneumoniae at 24%; and methicillin-resistant S. aureus at 11%), although the limited community-level evidence base call for more granular, representative surveys to inform public health policy.Moreover, comparisons of AMR prevalence across time periods were only feasible for K. pneumoniae, E. coli, A. baumannii and P. aeruginosa in hospital settings, due to limited available data for the other pathogens.
A range of complex drivers render Indonesia particularly vulnerable to antibiotic-resistant bacteria.The widespread and weakly regulated use of antimicrobial agents in human and veterinary medicine and aquaculture for therapeutic or prophylactic purposes is the main driver of the acquisition and selection of antibioticresistant bacteria. 1,3Based on pharmaceutical sales data, Indonesia has seen an estimated 2.5-fold increase in nationwide antibiotic consumption between 2000 and 2015, mostly broad-spectrum penicillins, fluoroquinolones and cephalosporins, placing Indonesia among the greatest risers in antibiotic consumption globally (ranked 29th of 76 countries analysed). 30A recent literature review found the appropriateness of antibiotic prescribing to be low, coupled with widespread over-thecounter use of non-prescription antibiotics. 31In many underdeveloped, both urban and rural, settings across Indonesia, AMR emergence and spread is likely exacerbated by inadequate sanitation and hygiene, poor infection prevention and control in health care facilities, and lack of awareness of AMR in communities and among healthcare providers. 31,32he implementation of Indonesia's 2020-2024 National Action Plan on AMR has shown progress in strengthening national capacities for microbiological laboratories and surveillance, 33 renewed national guidelines for antibiotic prescribing 34 and recent regulations for hospital antimicrobial stewardship programmes. 35Nonetheless, to build sustainable capacity to contain AMR, a recent analysis identified several urgent policy priorities, that include implementing nationwide surveillance of AMR and antimicrobial consumption and use; evidencebased antimicrobial stewardship and infection prevention and control programmes; developing regulatory frameworks to control antimicrobial use; amongst others. 36Further investments will be especially needed to strengthen the quality of primary health care delivery, diagnostic laboratory capacities, and health Panel: A comparison between Indonesia, other Southeast Asian countries and the European Union of AMR prevalence estimates for key pathogenantimicrobial combinations in hospitalised populations.information systems, with particular attention to equitable access to timely and reliable infection diagnostics and appropriate antibiotic prescribing across all healthcare settings.There are several limitations to this review.First, the substantial variability in populations, methodology and laboratory methods between studies limited the ability for data aggregation.Although most studies were based on routinely collected clinical specimens, the AMR data included in this systematic review were not derived from representative sentinel surveillance sites, as recommended by GLASS, which may have introduced several potential biases that may have led to over-or underestimation of AMR prevalence.Because we had to rely on aggregated, author-reported AMR data for most studies, we could not ascertain the clinical significance of bacterial isolates and we may thus have included nonpathogenic colonizing pathogens, for which antimicrobial susceptibilities may differ.GLASS acknowledges this risk as an inherent limitation to their approach. 12onetheless, the main AMR estimates in the systematic review were in the same range as those reported in previous reports (not included in this review), as summarised in the Panel, suggesting that any potential bias from including colonizing pathogens may have been limited.Second, in the Indonesian context, microbiology laboratory capability has been underdeveloped and culture utilization has been very low. 15,16Therefore, the peer-reviewed literature on AMR epidemiology may overrepresent hospitals that have microbiological laboratory capacity, as well as complex patient populations with high antibiotic exposure and who therefore have a higher risk of a drug-resistant infection.Third, the absence of data on laboratory quality assurance, standardised test panels of antibiotics, the lack of reported minimal inhibitory concentrations and the use of different CLSI/EUCAST versions during the study period (2000-2023) may have introduced heterogeneity in AST interpretation.This issue has been previously recognised and we therefore applied the MICRO checklist to ensure that we reported the microbiological data transparently. 13Although guidelines for clinical breakpoints were not available for some pathogenantimicrobial combinations (e.g.fosfomycin for K. pneumoniae), we opted to include them for the purpose of this epidemiological evidence synthesis.Lastly, although our comprehensive search strategy included the main national and global bibliographic databases, we cannot rule out that we may have missed other relevant publications.Furthermore, the exclusion of non-peerreviewed, grey literature meant that we might not have included useful evidence from other sources. 8,10However, their exclusion likely improved the quality of the evidence as grey literature may not always follow recommended quality standards for evaluation.
In conclusion, AMR prevalence estimates in common and medically important gram-negative bacteria in Indonesia are among the highest reported in the Southeast Asian region.More representative, granular, high-quality and standardised AMR data are required to construct accurate AMR estimates for all GLASSspecific pathogens, geographic areas, across the national, province, district and health facility levels.This information can inform locally relevant empiric treatment guidelines and effective public health policies and interventions, and guide priorities for the National Action Plan on AMR.
Contributors MG and RLH conceptualised the study.MG, DMS, RL, HRVD and RLH designed the study protocol and data extraction instrument.MG, GL, DMS and RS collected and verified the data, overseen by RL, HRVD and RLH.MG, GL and RLH performed the data analysis and had full access to all study data.MG, GL, RS, and RLH drafted the paper, with critical inputs from YRS, RL, EJN, HRVD, and AK.All authors had full access to all the data in the study, critically revised the manuscript, accept responsibility to submit for publication, and gave approval for the final version to be published.

Data sharing statement
No additional data are available.

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Declaration of interests
HRVD serves as Board Member of The Wellcome Surveillance and Epidemiology of Drug-resistant Infections Consortium (SEDRIC).AK serves as the current Chair of the National AMR Committee (Komite Pengendalian Resistensi Antimikroba).The other authors declare no competing interests.

21 Fig. 2 :Fig. 3 :
Fig. 2: Geographical map of the 102 reports included in the review.The map includes 10 studies that were conducted in multiple provinces (4 in hospitals, 3 in communities, and 3 in both).

Fig. 4 :
Fig.4: AMR prevalence estimates for GLASS-specific pathogen-antimicrobial combinations in hospital and community settings.Figure shows bar charts of AMR prevalence estimates for GLASS-specific pathogen-antimicrobial combinations, for hospitals (dark color) and communities (light color), accounting for sampling weights of the individual studies (see Supplementary TableS4for further details).95% confidence intervals were estimated using Wilson score interval (where the number of studies was greater than one).a The estimate for third-generation cephalosporins is derived only from ceftazidime.bResistance to methicillin detected using either cefoxitin or oxacillin.cThe estimate for fluoroquinolones is derived only from ciprofloxacin.d The estimate for fluoroquinolones is derived only from levofloxacin.
Fig.4: AMR prevalence estimates for GLASS-specific pathogen-antimicrobial combinations in hospital and community settings.Figure shows bar charts of AMR prevalence estimates for GLASS-specific pathogen-antimicrobial combinations, for hospitals (dark color) and communities (light color), accounting for sampling weights of the individual studies (see Supplementary TableS4for further details).95% confidence intervals were estimated using Wilson score interval (where the number of studies was greater than one).a The estimate for third-generation cephalosporins is derived only from ceftazidime.bResistance to methicillin detected using either cefoxitin or oxacillin.cThe estimate for fluoroquinolones is derived only from ciprofloxacin.d The estimate for fluoroquinolones is derived only from levofloxacin.

Table 1 :
Characteristics of included reports.
Fig.4, Supplementary Fig.S1and TableS8).Across the six hospital reports that distinguished HAI and CAI, the percentage of E. coli isolates exhibiting resistance against 3GC was 76.1% (70.9-80.6;204/270) and 30.0%(3/10); carbapenems 13.9% (10.1-19.0; Figure shows tornado plots of (A) the number of included reports that described resistance data for each of the GLASS bacterial pathogens (102 overall; 73 from hospitals, 24 from communities, 5 from both); and (B) the number of included bacterial isolates for each of the GLASS bacterial pathogens (19,517 overall; 13,647 from hospitals and 5870 from communities), stratified by hospitals (blue) and communities (purple).Data are sorted in descending order of number of total reports and isolates in hospitals and communities.3% (5/19); and tobramycin 46.6% (27/58) and unknown (no CAI data) respectively.Compared to